metadata

title: Levenshtein distance
emoji: ✍️
colorFrom: blue
colorTo: green
tags:
  - evaluate
  - metric
description: Levenshtein (edit) distance
sdk: gradio
sdk_version: 5.6.0
app_file: app.py
pinned: false

Metric Card for the Levenshtein (edit) distance

Metric Description

This metric computes the Levenshtein distance, also commonly called "edit distance". The Levenshtein distance measures the number of combined editions, deletions and additions to perform on a string so that it becomes identical to a second one. It is a popular metric for text similarity. This module directly calls the Levenshtein package for fast execution speed.

How to Use

Inputs

List all input arguments in the format below

predictions (string): sequence of prediction strings
references (string): sequence of reference string;
kwargs keyword arguments to pass to the Levenshtein.distance method.

Output Values

Dictionary mapping to the average Levenshtein distance (lower is better) and the ratio ([0, 1]) distance (higher is better).

Examples

import evaluate

levenshtein = evaluate.load("Natooz/Levenshtein")
results = levenshtein.compute(
    predictions=[
        "foo", "baroo"  # 0 and 2 edits
    ],
    references=[
        "foo", "bar"
    ],
)
print(results)
# {"levenshtein": 1, "levenshtein_ratio": 0.875}

Citation

@ARTICLE{1966SPhD...10..707L,
       author = {{Levenshtein}, V.~I.},
        title = "{Binary Codes Capable of Correcting Deletions, Insertions and Reversals}",
      journal = {Soviet Physics Doklady},
         year = 1966,
        month = feb,
       volume = {10},
        pages = {707},
       adsurl = {https://ui.adsabs.harvard.edu/abs/1966SPhD...10..707L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}